Publications by authors named "Chenwei Shi"

Urban parks play a significant role in urban ecosystems and are strongly associated with human health. Nevertheless, the biological contamination of urban parks - opportunistic pathogens and antibiotic resistance genes (ARGs) - has been poorly reported. Here, metagenomic and 16 S rRNA sequencing methods were used to study the distribution and assembly of opportunistic pathogens and ARGs in soil and water from nine parks in Lanzhou city, and further compared them with local human gut microbiomes to investigate the potential transmission risk.

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Batteries may degrade fast at extreme temperatures, posing a challenge in meeting the dual requirements of heat preservation at low temperatures and efficient cooling at high temperatures. To address this issue, we propose a cavity structure-based active controllable thermal switch. It has a potential switch ratio (SR) of approximately 300, with an experimental SR of 15.

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Introduction: Decoding brain activities is one of the most popular topics in neuroscience in recent years. And deep learning has shown high performance in fMRI data classification and regression, but its requirement for large amounts of data conflicts with the high cost of acquiring fMRI data.

Methods: In this study, we propose an end-to-end temporal contrastive self-supervised learning algorithm, which learns internal spatiotemporal patterns within fMRI and allows the model to transfer learning to datasets of small size.

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Human-health risks from microplastics have attracted considerable attention, but little is known about human-exposure pathways and intensities. Recent studies posited that inhalation of atmospheric microplastics was the dominant human-exposure pathway. Herein, our study identified that atmospheric microplastics ingested from deposition during routine dining/drinking activities represent another important exposure pathway.

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Decoding brain cognitive states from neuroimaging signals is an important topic in neuroscience. In recent years, deep neural networks (DNNs) have been recruited for multiple brain state decoding and achieved good performance. However, the open question of how to interpret the DNN black box remains unanswered.

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Airborne microplastics (MPs) are receiving increasing attention due to their ubiquitous nature and the potential human health consequences resulting from inhalation. The limited data for airborne MP concentrations vary widely among studies (∼4 orders of magnitude), but comparisons are tenuous due to the inconsistent collection and detection/enumeration methodologies among studies. Herein, we used uniform methodologies to obtain comparable airborne MP concentration data to assess MP exposure intensity in five Chinese megacities.

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